1. Technical Field
The embodiments herein generally relate to computerized image systems and, more particularly, to detection systems for comparing changes in imagery.
2. Description of the Related Art
The highly variable nature of radar measurements has plagued automatic target detection algorithm developers for decades. In particular, the variability introduced (in certain circumstances) by system noise and synthetic aperture processing compound the problem for small targets. While a change detection mode of operation may allow the detector to combat these effects, the false alarm rate can still remain unacceptably high. This is even true when the highly popular ratio-based tests are applied to synthetic aperture radar (SAR) systems.
One method of addressing this problem is to exploit spatial or temporal averaging to reduce the variance of the underlying random phenomena. If only a single pass, or look, is available for this purpose, then multiple looks for averaging must be synthesized, either through spatial averaging or sub-aperture processing. This implies that the resulting multi-look averaged SAR image will likely have lower resolution than the non-averaged one. Such a trade-off, however, is often reasonable, especially if the targets of intent comprise several image pixels. Even with spatial or temporal averaging, however, the false alarm rate often remains too high.
One can also address the false alarm problem by setting various thresholds to eliminate (in some way) clearly unsuitable samples from consideration. These thresholds can be incorporated either as floors for the denominator and numerator values used to form the ratio, or as “gatekeepers” for considering pixel locations as possible change locations. If a pixel location fails to pass the “gatekeeper”, it is eliminated from consideration. Unfortunately, such an algorithmic modification adds complexity, and while it should effectively eliminate certain false alarms, it also creates new parameters—the threshold—that must be determined. For a radar system operating at an extremely low grazing angle, however, the benefits of false alarm reduction may well outweigh any drawbacks due to increased algorithmic complexity. Under these operating conditions, the SAR images produced by the system would most likely contain long shadows and other regions of low intensity due to heavy forward scattering. Hence, the denominator of the ratio test would often contain small values, and the resulting ratio would be artificially large.
A change detection system compares imagery (i.e. a two-dimensional representation of a scene) collected at different times to determine if any changes have occurred. Typically, the image pixels indicate the amount of energy reflected or emitted from a particular location (e.g. a photograph or a synthetic aperture radar image). One commonly implemented change detection system utilizes a ratio test, calculating the ratio of pixel values from the same location and comparing this ratio to a pre-determined threshold. In fact, ratio-based change detection has been shown to be optimal under certain operating conditions. The system designates locations with pixel ratios exceeding the threshold as being change locations. Unfortunately, such a system is unable to operate effectively in the presence of noisy, imperfect image data. Some attempts to modify the system have been made, but these approaches generally are ad hoc and fail to provide a systematic approach to the problem.